A new gradient free local search mechanism for constrained multi-objective optimization problems

Uribe, Lourdes; Lara, Adriana; Deb, Kalyanmoy; Schütze, Oliver

doi:10.1016/j.swevo.2021.100938

Cited by 9 publications

(3 citation statements)

References 34 publications

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“…Constraints have not been considered in the push stage, while an improved epsilonconstraint method is used in the pull phase. To improve the search performance of MOEAs, a local search mechanism [12] was proposed, which can contain constraint information and does not need to explicitly calculate gradient information. Liu et al [13] developed an indicator-based CMOEA framework, in which indicator-based MOEAs and CHTs are effectively combined to solve CMOPs.…”

Section: Literature Review 21 Constrained Multi-objective Evolutionar...mentioning

confidence: 99%

“…In lines 1 to 2, according to AC, the number of individuals choosing each CHT can be determined. Therefore, mIGD value can be calculated by Equation (12). In lines 3 to 6, the maximum mIGD value represents the individual choosing this CHT in the "poor" state and its reward is −1; the middle mIGD value indicates the state is "medium" and its reward is 0; and the reward of "excellent" CHT is 1.…”

Section: Adaptive Constraint Handling Technologymentioning

confidence: 99%

“…Step 2: perform the current action a in the current state s. According to the generation strategy chain GC, the number of individuals selecting each generation strategy can be determined. The mIGD value can be calculated by Equation (12).…”

Section: Adaptive Generation Strategymentioning

confidence: 99%

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Self-Adaptive Constrained Multi-Objective Differential Evolution Algorithm Based on the State–Action–Reward–State–Action Method

Liu¹,

Cui²,

Fan³

2022

Mathematics

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The performance of constrained multi-objective differential evolution algorithms (CMOEAs) is mainly determined by constraint handling techniques (CHTs) and their generation strategies. To realize the adaptive adjustment of CHTs and generation strategies, an adaptive constrained multi-objective differential evolution algorithm based on the state–action–reward–state–action (SARSA) approach (ACMODE) is introduced in the current study. In the proposed algorithm, the suitable CHT and the appropriate generation strategy can be automatically selected via a SARSA method. The performance of the proposed algorithm is compared with four other famous CMOEAs on five test suites. Experimental results show that the overall performance of the ACMODE is the best among all competitors, and the proposed algorithm is capable of selecting an appropriate CHT and a suitable generation strategy to solve a particular type of constrained multi-objective optimization problems.

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Section: Literature Review 21 Constrained Multi-objective Evolutionar...mentioning

confidence: 99%

Section: Adaptive Constraint Handling Technologymentioning

confidence: 99%